01017nas a2200205 4500008004100000245002500041210002400066300001400090520048400104653002300588653000800611653001400619653002400633100001800657700002000675700002700695700002000722700001800742856005100760 2014 eng d00aEL-ifying Ontologies0 aELifying Ontologies a464–4793 a
The OWL 2 profiles are fragments of the ontology language OWL 2 for which standard reasoning tasks are feasible in polynomial time. Many OWL ontologies, however, contain a typically small number of out-of-profile axioms, which may have little or no influence on reasoning outcomes. We investigate techniques for rewriting axioms into the EL and RL profiles of OWL 2. We have tested our techniques on both classification and data reasoning tasks with encouraging results.
10adescription logics10aOWL10aRewriting10aTractable Reasoning1 aCarral, David1 aFeier, Cristina1 aGrau, Bernardo, Cuenca1 aHitzler, Pascal1 aHorrocks, Ian uhttp://dx.doi.org/10.1007/978-3-319-08587-6_3601073nas a2200193 4500008004100000245005900041210005900100300001400159520049700173653002300670653000800693653002400701100001800725700002000743700002700763700002000790700001800810856005100828 2014 eng d00aPushing the Boundaries of Tractable Ontology Reasoning0 aPushing the Boundaries of Tractable Ontology Reasoning a148–1633 aWe identify a class of Horn ontologies for which standard reasoning tasks such as instance checking and classification are tractable. The class is general enough to include the OWL 2 EL, QL, and RL profiles. Verifying whether a Horn ontology belongs to the class can be done in polynomial time. We show empirically that the class includes many real-world ontologies that are not included in any OWL 2 profile, and thus that polynomial time reasoning is possible for these ontologies.
10adescription logics10aOWL10aTractable Reasoning1 aCarral, David1 aFeier, Cristina1 aGrau, Bernardo, Cuenca1 aHitzler, Pascal1 aHorrocks, Ian uhttp://dx.doi.org/10.1007/978-3-319-11915-1_1001419nas a2200205 4500008004100000245008100041210006900122300001400191520078200205653002300987653000801010653002401018100001801042700002001060700002301080700002701103700002001130700001801150856004501168 2014 eng d00aIs Your Ontology as Hard as You Think? Rewriting Ontologies into Simpler DLs0 aYour Ontology as Hard as You Think Rewriting Ontologies into Sim a128–1403 aWe investigate cases where an ontology expressed in a seemingly hard DL can be polynomially reduced to one in a simpler logic, while preserving reasoning outcomes for classification and fact entailment. Our transformations target the elimination of inverse roles, universal and existential restrictions, and in the best case allow us to rewrite the given ontology into one of the OWL 2 profiles. Even if an ontology cannot be fully rewritten into a profile, in many cases our transformations allow us to exploit further optimisation techniques. Moreover, the elimination of some out-of-profile axioms can improve the performance of modular reasoners, such as MORe. We have tested our techniques on both classification and data reasoning tasks with encouraging results.
10adescription logics10aOWL10aTractable Reasoning1 aCarral, David1 aFeier, Cristina1 aRomero, Ana, Armas1 aGrau, Bernardo, Cuenca1 aHitzler, Pascal1 aHorrocks, Ian uhttp://ceur-ws.org/Vol-1193/paper_75.pdf01947nas a2200241 4500008004100000245006000041210005700101300001400158520126400172653002801436653000801464653001501472100001601487700002401503700001801527700002001545700001701565700002101582700002001603700001501623700001601638856005101654 2013 eng d00aA Geo-ontology Design Pattern for Semantic Trajectories0 aGeoontology Design Pattern for Semantic Trajectories a438–4563 aTrajectory data have been used in a variety of studies, including human behavior analysis, transportation management, and wildlife tracking. While each study area introduces a different perspective, they share the need to integrate positioning data with domain-specific information. Semantic annotations are necessary to improve discovery, reuse, and integration of trajectory data from different sources. Consequently, it would be beneficial if the common structure encountered in trajectory data could be annotated based on a shared vocabulary, abstracting from domain-specific aspects. Ontology design patterns are an increasingly popular approach to define such flexible and self-contained building blocks of annotations. They appear more suitable for the annotation of interdisciplinary, multi-thematic, and multi-perspective data than the use of foundational and domain ontologies alone. In this paper, we introduce such an ontology design pattern for semantic trajectories. It was developed as a community effort across multiple disciplines and in a data-driven fashion. We discuss the formalization of the pattern using the Web Ontology Language (OWL) and apply the pattern to two different scenarios, personal travel and wildlife monitoring.
10aOntology Design Pattern10aOWL10aTrajectory1 aHu, Yingjie1 aJanowicz, Krzysztof1 aCarral, David1 aScheider, Simon1 aKuhn, Werner1 aBerg-Cross, Gary1 aHitzler, Pascal1 aDean, Mike1 aKolas, Dave uhttp://dx.doi.org/10.1007/978-3-319-01790-7_24